SoroushVahidi/scidocs huggingface dataset:
Parental delay or refusal of vaccine doses, childhood vaccination coverage at 24 months of age, and the Health Belief Model.
(2011)

OBJECTIVE
We evaluated the association between parents' beliefs about vaccines, their decision to delay or refuse vaccines for their children, and vaccination coverage of children at aged 24 months.


METHODS
We used data from 11,206 parents of children aged 24-35 months at the time of the 2009 National Immunization Survey interview and determined their vaccination status at aged 24 months. Data included parents' reports of delay and/or refusal of vaccine doses, psychosocial factors suggested by the Health Belief Model, and provider-reported up-to-date vaccination status.


RESULTS
In 2009, approximately 60.2% of parents with children aged 24-35 months neither delayed nor refused vaccines, 25.8% only delayed, 8.2% only refused, and 5.8% both delayed and refused vaccines. Compared with parents who neither delayed nor refused vaccines, parents who delayed and refused vaccines were significantly less likely to believe that vaccines are necessary to protect the health of children (70.1% vs. 96.2%), that their child might get a disease if they aren't vaccinated (71.0% vs. 90.0%), and that vaccines are safe (50.4% vs. 84.9%). Children of parents who delayed and refused also had significantly lower vaccination coverage for nine of the 10 recommended childhood vaccines including diphtheria-tetanus-acellular pertussis (65.3% vs. 85.2%), polio (76.9% vs. 93.8%), and measles-mumps-rubella (68.4% vs. 92.5%). After adjusting for sociodemographic differences, we found that parents who were less likely to agree that vaccines are necessary to protect the health of children, to believe that their child might get a disease if they aren't vaccinated, or to believe that vaccines are safe had significantly lower coverage for all 10 childhood vaccines.


CONCLUSIONS
Parents who delayed and refused vaccine doses were more likely to have vaccine safety concerns and perceive fewer benefits associated with vaccines. Guidelines published by the American Academy of Pediatrics may assist providers in responding to parents who may delay or refuse vaccines.
An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach
(2017)

Credit scoring is a decision tool used by organizations to grant or reject credit requests from their customers. Series of artificial intelligent and traditional approaches have been used to building credit scoring model and credit risk evaluation. Despite being ranked amongst the top 10 algorithm in Data mining, Naïve Bayesian algorithm has not been extensively used in building credit score cards. Using demographic and material indicators as input variables, this paper investigate the ability of Bayesian classifier towards building credit scoring model in banking sector.
Political Issue Extraction Model: A Novel Hierarchical Topic Model That Uses Tweets By Political And Non-Political Authors
(2016)

People often use social media to discuss opinions, including political ones. We refer to relevant topics in these discussions as political issues, and the alternate stands towards these topics as political positions. We present a Political Issue Extraction (PIE) model that is capable of discovering political issues and positions from an unlabeled dataset of tweets. A strength of this model is that it uses twitter timelines of political and non-political authors, and affiliation information of only political authors. The model estimates word-specific distributions (that denote political issues and positions) and hierarchical author/group-specific distributions (that show how these issues divide people). Our experiments using a dataset of 2.4 million tweets from the US show that this model effectively captures the desired properties (with respect to words and groups) of political discussions. We also evaluate the two components of the model by experimenting with: (a) Use to alternate strategies to classify words, and (b) Value addition due to incorporation of group membership information. Estimated distributions are then used to predict political affiliation with 68% accuracy.
Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample
(1987)

A mailed questionnaire was used to obtain comparative risk judgments for 32 different hazards from a random sample of 296 individuals living in central New Jersey. The results demonstrate that an optimistic bias about susceptibility to harm-a tendency to claim that one is less at risk than one's peers—is not limited to any particular age, sex, educational, or occupational group. It was found that an optimistic bias is often introduced when people extrapolate from their past experience to estimate their future vulnerability. Thus, the hazards most likely to elicit unrealistic optimism are those associated with the belief (often incorrect) that if the problem has not yet appeared, it is unlikely to occur in the future. Optimistic biases also increase with the perceived preventability of a hazard and decrease with perceived frequency and personal experience. Other data presented illustrate the inconsistent relationships between personal risk judgments and objective risk factors.
The cultural construction of self-enhancement: an examination of group-serving biases.
(1997)

Self-serving biases, found routinely in Western samples, have not been observed in Asian samples. Yet given the orientation toward individualism and collectivism in these 2 cultures, respectively, it is imperative to examine whether parallel differences emerge when the target of evaluation is the group. It may be that Asians show a group-serving bias parallel to the Western self-serving bias. In 2 studies, group-serving biases were compared across European Canadian, Asian Canadian, and Japanese students. Study 1 revealed that Japanese students evaluated a family member less positively than did both groups of Canadian students. Study 2 replicated this pattern with students' evaluations of their universities. The data suggest that cultural differences in enhancement biases are robust, generalizing to individuals' evaluations of their groups.
Unrealistic optimism about susceptibility to health problems
(1982)

In this study, 100 college students compared their own chances of experiencing 45 different health- and life-threatening problems with the chances of their peers. They showed a significant optimistic bias for 34 of these hazards, consistently considering their own chances to be below average. Attempts to account for the amount of bias evoked by different hazards identified perceived controllability, lack of previous experience, and the belief that the problem appears during childhood as factors that tend to increase unrealistic optimism. The investigation also examined the importance of beliefs and emotions as determinants of self-reported interest in adopting precautions to reduce one's risk. It found that: (a) beliefs about risk likelihood, beliefs about risk severity, and worry about the risk all made independent contributions to interest in risk reduction; (b) unrealistic optimism undermined interest in risk reduction indirectly by decreasing worry; and (c) beliefs about risk likelihood and severity were not sufficient to explain the amount of worry expressed about different hazards.
Judgment under Uncertainty: Heuristics and Biases.
(1974)

This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
Fault tolerant parallel data-intensive algorithms
(2012)

Fault-tolerance is rapidly becoming a crucial issue in high-end and distributed computing, as increasing number of cores are decreasing the mean-time to failure of the systems. In this work, we present an algorithm-based fault tolerance solution that handles fail-stop failures for a class of iterative data intensive algorithms. We intelligently replicate the data to minimize data loss in multiple failures and decrease re-execution in recovery by little modifications in the algorithms. We evaluate our approach by using two data mining algorithms, kmeans and Apriori. We show that our approach has negligible overhead and allows us to gracefully handle different number of failures. In addition, our approach outperforms Hadoop both in absence and presence of failures.
Confidence sets for persistence diagrams
(2013)

Persistent homology is a method for probing topological properties of point clouds and functions. The method involves tracking the birth and death of topological features as one varies a tuning parameter. Features with short lifetimes are informally considered to be “topological noise,” and those with a long lifetime are considered to be “topological signal.” In this paper, we bring some statistical ideas to persistent homology. In particular, we derive confidence sets that allow us to separate topological signal from topological noise.
Word Semantic Representations using Bayesian Probabilistic Tensor Factorization
(2014)

Many forms of word relatedness have been developed, providing different perspectives on word similarity. We introduce a Bayesian probabilistic tensor factorization model for synthesizing a single word vector representation and per-perspective linear transformations from any number of word similarity matrices. The resulting word vectors, when combined with the per-perspective linear transformation, approximately recreate while also regularizing and generalizing, each word similarity perspective. Our method can combine manually created semantic resources with neural word embeddings to separate synonyms and antonyms, and is capable of generalizing to words outside the vocabulary of any particular perspective. We evaluated the word embeddings with GRE antonym questions, the result achieves the state-ofthe-art performance.
Facial performance enhancement using dynamic shape space analysis
(2014)

The facial performance of an individual is inherently rich in subtle deformation and timing details. Although these subtleties make the performance realistic and compelling, they often elude both motion capture and hand animation. We present a technique for adding fine-scale details and expressiveness to low-resolution art-directed facial performances, such as those created manually using a rig, via marker-based capture, by fitting a morphable model to a video, or through Kinect reconstruction using recent faceshift technology. We employ a high-resolution facial performance capture system to acquire a representative performance of an individual in which he or she explores the full range of facial expressiveness. From the captured data, our system extracts an expressiveness model that encodes subtle spatial and temporal deformation details specific to that particular individual. Once this model has been built, these details can be transferred to low-resolution art-directed performances. We demonstrate results on various forms of input; after our enhancement, the resulting animations exhibit the same nuances and fine spatial details as the captured performance, with optional temporal enhancement to match the dynamics of the actor. Finally, we show that our technique outperforms the current state-of-the-art in example-based facial animation.
Improving Distant Supervision with Maxpooled Attention and Sentence-Level Supervision
(2018)

We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FBNYT dataset.
Enhanced Handover Decision Algorithm in Heterogeneous Wireless Network
(2017)

Transferring a huge amount of data between different network locations over the network links depends on the network's traffic capacity and data rate. Traditionally, a mobile device may be moved to achieve the operations of vertical handover, considering only one criterion, that is the Received Signal Strength (RSS). The use of a single criterion may cause service interruption, an unbalanced network load and an inefficient vertical handover. In this paper, we propose an enhanced vertical handover decision algorithm based on multiple criteria in the heterogeneous wireless network. The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. The simulation results illustrate that the three types of decision algorithms outperform the traditional network decision algorithm in terms of handover number probability and the handover failure probability. In addition, it is noticed that the network priority handover decision algorithm produces better results compared to the equal priority and the mobile priority handover decision algorithm. Finally, the simulation results are validated by the analytical model.
A FRAMEWORK FOR DESIGNING MOBILE QURANIC MEMORIZATION TOOL USING MULTIMEDIA INTERACTIVE LEARNING METHOD FOR CHILDREN
(2016)

Quran is the fundamental holy book of Islam. Among the most important concerns is to learn it by heart. However, current method in Quranic schools is becoming less effective towards young generation. Thus, there is a need to find alternative solutions to memorize Quran for young Muslims. Mobile learning is an alternative to conventional learning that will support the existing method. Mobile devices have made an immediate impact on teaching and learning practices. However, for mobile learning to be effective for children in memorizing Quran, it is necessary to find specific design guidelines and pedagogy to this learning method. This paper aims at providing a unifying framework for developing Quran memorizer application using multimedia interactive method and learning theories for mobile learning.
The WHO public health approach to HIV treatment and care: looking back and looking ahead.
(2017)

In 2006, WHO set forth its vision for a public health approach to delivering antiretroviral therapy. This approach has been broadly adopted in resource-poor settings and has provided the foundation for scaling up treatment to over 19·5 million people. There is a global commitment to end the AIDS epidemic as a public health threat by 2030 and, to support this goal, there are opportunities to adapt the public health approach to meet the ensuing challenges. These challenges include the need to improve identification of people with HIV infection through expanded approaches to testing; further simplify and improve treatment and laboratory monitoring; adapt the public health approach to concentrated epidemics; and link HIV testing, treatment, and care to HIV prevention. Implementation of these key public health principles will bring countries closer to the goals of controlling the HIV epidemic and providing universal health coverage.
The effects of students' motivation, cognitive load and learning anxiety in gamification software engineering education: a structural equation modeling study
(2015)

Past research has proven the significant effects of game-based learning on learning motivation and academic performance, and described the key factors in game-based design. Nonetheless, research on the correlations among learning motivation, cognitive load, learning anxiety and academic performance in gamified learning environments has been minimal. This study, therefore, aims to develop a Gamification Software Engineering Education Learning System (GSEELS) and evaluate the effects of gamification, learning motivation, cognitive load and learning anxiety on academic performance. By applying Structural Equation Modeling (SEM) to the empirical research, the questionnaire contains: 1. a Gamification Learning Scale; 2. a Learning Motivation Scale; 3. a Cognitive Load Scale; 4. a Learning Anxiety Scale; and 5. an Academic Performance Scale. A total of 107 undergraduates in two classes participated in this study. The Structural Equation Modeling (SEM) analysis includes the path directions and relationship between descriptive statistics, measurement model, structural model evaluation and five variables. The research results support all nine hypotheses, and the research findings also show the effects of cognitive load on learning anxiety, with strong learning motivation resulting from a low learning anxiety. As a result, it is further proven in this study that a well-designed GSEELS would affect student learning motivation and academic performance. Finally, the relationship model between gamification learning, learning motivation, cognitive load, learning anxiety and academic performance is elucidated, and four suggestions are proffered for instructors of software engineering education courses and for further research, so as to assist instructors in the application of favorable gamification teaching strategies.
Towards a global participatory platform : democratising open data , complexity science and collective intelligence
(2012)

The FuturICT project seeks to use the power of big data, analytic models grounded in complexity science, and the collective intelligence they yield for societal benefit. Accordingly, this paper argues that these new tools should not remain the preserve of restricted 110 The European Physical Journal Special Topics government, scientific or corporate élites, but be opened up for societal engagement and critique. To democratise such assets as a public good, requires a sustainable ecosystem enabling different kinds of stakeholder in society, including but not limited to, citizens and advocacy groups, school and university students, policy analysts, scientists, software developers, journalists and politicians. Our working name for envisioning a sociotechnical infrastructure capable of engaging such a wide constituency is the Global Participatory Platform (GPP). We consider what it means to develop a GPP at the different levels of data, models and deliberation, motivating a framework for different stakeholders to find their ecological niches at different levels within the system, serving the functions of (i) sensing the environment in order to pool data, (ii) mining the resulting data for patterns in order to model the past/present/future, and (iii) sharing and contesting possible interpretations of what those models might mean, and in a policy context, possible decisions. A research objective is also to apply the concepts and tools of complexity science and social science to the project’s own work. We therefore conceive the global participatory platform as a resilient, epistemic ecosystem, whose design will make it capable of selforganization and adaptation to a dynamic environment, and whose structure and contributions are themselves networks of stakeholders, challenges, issues, ideas and arguments whose structure and dynamics can be modelled and analysed.
Comparison of supervised learning methods for spike time coding in spiking neural networks
(2006)

In this review we focus our attention on the supervised learning methods for spike time coding in Spiking Neural Networks (SNN). This study is motivated by the recent experimental results on information coding in the biological neural systems which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We pose a fundamental question, what paradigms of neural temporal coding can be implemented with the recent learning methods. In order to answer this question we discuss various approaches to the considered learning task. We shortly describe the particular learning algorithms and report the results of experiments. Finally we discuss properties, assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature.
Enhanced parallel cat swarm optimization based on the Taguchi method
(2012)

In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the Liu, Chen, and Chou method (2009). The optimal solutions can be found by the proposed EPCSO method in a very short time. 2011 Elsevier Ltd. All rights reserved.
Conducting a meta-ethnography of qualitative literature: Lessons learnt
(2008)

BACKGROUND
Qualitative synthesis has become more commonplace in recent years. Meta-ethnography is one of several methods for synthesising qualitative research and is being used increasingly within health care research. However, many aspects of the steps in the process remain ill-defined.


DISCUSSION
We utilized the seven stages of the synthesis process to synthesise qualitative research on adherence to tuberculosis treatment. In this paper we discuss the methodological and practical challenges faced; of particular note are the methods used in our synthesis, the additional steps that we found useful in clarifying the process, and the key methodological challenges encountered in implementing the meta-ethnographic approach. The challenges included shaping an appropriate question for the synthesis; identifying relevant studies; assessing the quality of the studies; and synthesising findings across a very large number of primary studies from different contexts and research traditions. We offer suggestions that may assist in undertaking meta-ethnographies in the future.


SUMMARY
Meta-ethnography is a useful method for synthesising qualitative research and for developing models that interpret findings across multiple studies. Despite its growing use in health research, further research is needed to address the wide range of methodological and epistemological questions raised by the approach.